This description is based on a conversation with 'Sam', which took place over several days in January and February 2025
Before an LLM (large language model) is trained, it undergoes a critical pre-processing stage where vast amounts of text data are sampled, filtered, and de-duplicated to improve efficiency and reduce computational costs. Sampling ensures that only a subset of data is used, while de-duplication removes repetitive content to prevent models from overweighting certain patterns. These steps help streamline training, but they come with a tradeoff—by stripping away redundant or similar data, models may also lose subtle contextual layers that influence meaning and real-world accuracy.
For example, removing similar but slightly varied discussions on a topic could erase valuable distinctions—such as the way different experts frame an issue or how language evolves in different cultures and industries. Take the word sneakers—a user’s sentence containing it could have many different contextual meanings depending on surrounding words. "Sneakers on sale" suggests a retail setting, while "Sneakers for marathon training" indicates performance-based footwear, and "Sneakers banned in the workplace" suggests a discussion on dress codes. Without retaining these contextual variations, an AI model might generalize incorrectly, missing the nuances that define intent.
Likewise, de-duplication—especially within a single data source—removes the natural weight, or importance, of specific words and phrases within that dataset. This eliminates a critical layer of “human-like” context, making the model less attuned to real-world language patterns. Language is shaped by frequency, emphasis, and repetition in human communication; when those signals are stripped out, models may fail to recognize what matters most in a given context.
Augmetrics® solves this by preserving structured, reusable knowledge objects (IO/KOs) instead of relying solely on pre-processing reductions. Rather than discarding context, it ensures AI models retain a traceable, layered understanding of information, balancing efficiency with depth of knowledge. This approach helps prevent LLMs from making shallow inferences and instead enables them to generate responses grounded in richer, more reliable context.